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Clemence Hwarire 7 August 2012
By Clemence Hwarire
Loan Repayment and Credit Management of Small Businesses
A CASE STUDY OF A SOUTH AFRICAN COMMERCIAL BANK
Loan Repayment and Credit Management of Small Businesses A CASE - - PowerPoint PPT Presentation
Loan Repayment and Credit Management of Small Businesses A CASE - - PowerPoint PPT Presentation
Loan Repayment and Credit Management of Small Businesses A CASE STUDY OF A SOUTH AFRICAN COMMERCIAL BANK Clemence Hwarire 7 August 2012 By Clemence Hwarire Contents Introduction Obstacles hindering the growth of small businesses
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Small businesses have been cited as major players in economic development
in South Africa. As is the case in other developing countries, securing financing and loan repayments remains a challenge in this group of enterprises.
The loan recovery rate among small businesses reveal a worrying trend as
- bserved by the South African Trade and Industry minister Rob Davies in a
May 2010 Parliamentary Question and Answer session. Studies by the South African Micro-finance Apex Fund (SAMAF) and the National Empowerment Fund (NEF) attest to a similar trend where default rates of as high as 35% have been recorded (Timm, 2011:37).
Introduction
By Clemence Hwarire
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5 10 15 20 25 30 35 40 South Africa Angola Zambia China Brazil Uganda Ghana Survival rate of small businesses
ACCORDING TO THE GLOBAL ENTREPRENEURSHIP MONITOR (GEM) (2010:23)
Survival rate of small businesses
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Different Criteria Used
Annual turnover
Assets
Number of people employed. In contrast, South African banks do not use the number of employees when defining SMEs. The big four South African banks, namely Absa, Standard Bank, FNB and Nedbank, use annual turnover to define small businesses as shown in Table 1.1.
Bank Turnover(SMME) Absa R10 million Standard R10 million FNB R10 million Nedbank R7.5 million
Table 1.1: Definition of SMEs by South African Banks
Source: Absa, 2011; Standard Bank, 2011; FNB, 2011; Nedbank, 2011.
BANK
Term loan Overdraft Asset Base Finance Vehicle Asset
Finance
Revolving Credit
Absa
X X X X X
FNB
X X X X
Nedbank
X X X X
Standard
X X X X X
PRODUCT Funding products available to SMMEs
By Clemence Hwarire
Definition of Small Businesses
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Lack of access to finance (Insufficient working capital) Inadequate management and financial management skills Lack of Education and training Poor economic conditions Resource starvation Poorly thought-out business plans
By Clemence Hwarire
Obstacles hindering the growth of small businesses
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Interest rate
Gender
Indebtedness of owner/business
Size of loan
Period of loan
Location of the business
Age
Education and training
Sector of the business
Cash flow management
By Clemence Hwarire
Factors affecting loan repayments
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Credit Scoring Model The models are statistical in nature such as logistical regression analysis or discriminant analysis and more recently neural networks and Support Vector Machine (SVM). Credit scoring methods are used to estimate the likelihood of default based on historical data on loan performance and characteristics of the borrower. Accounting-based Model The methodology of the accounting-based approach is based on Multiple Discriminant Analysis (MDA) and logistic models that are the most useful in accounting-based variables for classifying company default. Survival-based Credit Scoring Model The aim of the survival analysis method is to measure the link between illustrative variables and survival. The bank can manage and monitor profitability of clients to the bank over a customer’s lifetime.
By Clemence Hwarire
SMME models used to evaluate loan applications
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A sample of 169 accounts was used for the purpose of this study, after excluding declined and “no deal” applications. The research analysed loan advances made by a South African bank to SMEs since the 2008/09 financial year. For the purpose of this study only approved and taken-up loan products were sampled before and up to the end of July 2009. Furthermore, loans granted after July 2009 were excluded in order to simplify the analysis in regard to age. The performance of these accounts was observed for the two years ending July 2011.
By Clemence Hwarire
Methodology
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Descriptive statistics
Frequency distribution
and percentages
Empirical analysis
With the assistance of E-
Views econometric software
The models were estimated using the “Logit model”.
Table 2.1 presents definitions and the a priori or expected signs based in underlying theory and assumptions on the dependent variables used in the equation 2.1 and 2.2.
By Clemence Hwarire
Definition of probability of Default 1 A default is defined as any missed or delayed payment of interest and/or principal according to global rating agencies Moody’s and Standard and Poor. Definition of probability of Default 2 Basel II definition: an account that is past due more than 90 days is classified as Default 2. Based
- n the above discussion, the two Logit models used to analyse the factors affecting the default are
specified as follows: With a personal relationship: PROBDEF2 = β0 + β1 AGEO + β2 BKBALNEG + β3 CUSTN + β4 IRABOVEPR + β5 LOANSIZEL + β6 LOANSIZEM + β7 LOANTERML + β8 LTABF + β9 LTTERM + β10 OWNERF + β11 OWNERMF + β12 PERSRELATN + β13 RACEB + μ, ….. (4.1) With a business relationship: PROBDEF2 = β0 + β1 AGEO + β2 BKBALNEG + β3 BUSRELATN + β4 CUSTN + β5 IRABOVEPR + β6 LOANSIZEL + β7 LOANSIZEM + β8 LOANTERML + β9 LTABF + β10 LTTERM + β11 OWNERF + β12 OWNERMF + + β13 RACEB + μ, ….. (4.2) Where β0 is a constant βi are coefficients to be estimated μ is an error term, while the dependent variables and independent variables used in the models are defined in Table 2.1. The dependent variables used in the Logit model (Equation 2.1 and Equation 2.1) are explained. All dependent variables are in binary forms with a value of “1” if true and “0” otherwise. To prevent dummy variable trap, the rule (M-1) was applied. According to Gujarati and Porter (2005), “For each qualitative regressor, the number of dummy variables introduced must be one less than the categories of that variable”.
Data Analysis
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By Clemence Hwarire
Variable Definition Expected Sign AGEO A dummy that takes the value of one if the age of the borrower is over 35 and zero otherwise.
- BKBALNEG
A dummy that takes the value of one if the bank balance is negative and zero otherwise. + BUSRELATN A dummy that takes the value of one if the borrower has no business relationship with the bank and zero
- therwise.
+ CUSTN A dummy that takes the value of one if the borrower is a new client and zero otherwise. + IRABOVEPR A dummy that takes the value of one if interest rate above prime at the time of taking up the loan and zero
- therwise.
+ LOANSIZEM A dummy that takes the value of one if a loan size is medium (R101 000 to R500 000). Interest rate above prime at the time of taking up the loan and zero otherwise. + / - LOANSIZEL A dummy that takes the value of one if a loan size is large (R500 001 and above). Interest rate above prime at the time of taking up the loan and zero otherwise. + LOANTERML A dummy that takes the value of one if a loan period is long term (more that 12 months) and zero
- therwise.
+ / - LTABF A dummy that takes the value of one if a loan type is Asset Based Finance and zero otherwise.
- LTTERM
A dummy that takes the value of one if a loan type is term loan and zero otherwise. + OWNERMF A dummy that takes the value of one if the owners of the business are both male and female and zero
- therwise.
- OWNERF
A dummy that takes the value of one if the owner of the business is female and zero otherwise.
- PERSRELATN
A dummy that takes the value of one if the borrower has no personal relationship with the bank and zero
- therwise.
+ RACEB A dummy that takes the value of one if the race of the borrower is black and zero otherwise. +
Table 2.1: Variables, definition and a priori expectation
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By Clemence Hwarire
PROBABILITY OF DEFAULT (Default 1) Frequency Percentage (%) Default 66 39 No default 103 61 Total 169 100 PROBABILITY OF DEFAULT (Default 2) Frequency Percentage (%) Default 47 28 No default 122 72 Total 169 100 GENDER Frequency Percentage (%) Male 90 53 Female 34 20 Both male & female 45 27 Total 169 100 AGE Frequency Percentage (%) 35 and below 34 20 Over 35 135 80 Total 169 100 RACE Frequency Percentage (%) White 105 62 Black 64 38 Total 169 100 LOAN TYPE Frequency Percentage (%) Asset-based finance 45 27 Overdraft 56 33 Term loan 68 40 Total 169 100 CUSTOMER TYPE Frequency Percentage (%) New 149 88 Old 20 12 Total 169 100 PERSONAL RELATIONSHIP AT THE TIME OF APPLICATION Frequency Percentage (%) Personal relationship 145 86 No personal relationship 24 14 Total 169 100
Table 3.1: Descriptive analysis of Data
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By Clemence Hwarire
MODEL 1 2 3 4 5 6 VARIABLE COEFFICIENT COEFFICIENT COEFFICIENT COEFFICIENT COEFFICIENT COEFFICIENT C AGEO 1.624068
- 0.861260**
1.597861
- 0.838131**
- 0.422852
- 0.289197
- 0.505393
- 0.236008
- 0.653770
- 0.326530
- 0.735583
- 0.274756
BKBALNEG
- 1.042912*
- 1.062233*
- 1.059022*
- 1.086153*
- 0.212296
- 0.233812
CUSTN 0.176496 0.216537 0.988465 1.096238 1.313277
- 0.679076
IRABOVEPR
- 0.230916
- 0.270973
- 0.194028
- 0.310362
- 0.447855
- 0.543518
LOANSIZEL 0.217227 0.191720 0.232438 0.157393 0.219309 0.148737 LOANSIZEM
- 0.280562
- 0.287455
- 0.428764
- 0.460838
- 0.427127
- 0.456683
LOANTERML
- 0.817740
- 0.729861
- 0.231880
- 0.036905
- 0.312497
- 0.123160
LTABF
- 0.412160
- 0.474691
- 0.993276
- 1.123716
- 0.936783
- 1.065554
LTTERM
- 0.048951
- 0.094731
- 0.222460
- 0.309156
- 0.140396
- 0.226768
OWNERF 0.041978 0.066125
- 0.095097
- 0.015541
- 0.134125
- 0.055598
OWNERMF
- 1.528456*
- 1.518116*
- 1.290439**
- 1.262959**
- 1.339270*
- 1.312119**
PERSRELATN 0.243623 0.529859 0.520357 RACEB 0.488784 0.500390 0.651838* 0.685148* 0.650653** 0.683287** BUSRELATN
- 0.188966
- 0.714080
- 0.679076
McFadden R-squared 0.162186 0.161456 0.157108 0.154573 0.159215 0.156650 S.D. dependent var 0.489320 0.489320 0.449398 0.449398 0.449398 0.449398 Akaike info criterion 1.286652 1.287628 1.162242 1.165239 1.183419 1.186452 Schwarz criterion 1.545933 1.546909 1.421523 1.424521 1.479741 1.482773 Hannan-Quinn criterion. 1.391873 1.392849 1.267463 1.270460 1.303672 1.306704
- Restr. Deviance
226.1172 226.1172 199.8108 199.8108 199.8108 199.8108 LR statistic 36.67303 36.50808 31.39192 30.88537 31.81287 31.30045 Prob(LR statistic) 0.000466 0.000495 0.002954 0.003504 0.006826 0.008006
*Significant at 5% level; **Significant at 10% level. Table 3.2: Summary of all the models
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Factors that are statistically significant
Negative Bank Balance. (BKBALNEG) Businesses owned by both sexes (OWNERMF) Race (RACEB)
By Clemence Hwarire
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SUMMARY The study found the default rate to be 28 per cent which confirms findings of the public development finance institutions which recorded similar trends. IMPLICATIONS OF FINDINGS AND RECOMMENDATIONS Race and gender cannot be used as selection criteria in South Africa as these two factors are deemed as economic discrimination. However, these two variables are very important.
By Clemence Hwarire
Summary, Conclusion and Recommendations
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By Clemence Hwarire
Recommendations Entrepreneurs
Establish a personal relationship with the bank.
Small businesses are encouraged to take small to medium loans.
Male-female business partnerships to reduce risk appetite.
Banks
The banks can create an
innovative fund to cater for small businesses where write-offs are not regarded as losses but as part of corporate social investment.
Increase Overdraft lending
base.
Increase
awareness in regard to cash flow and general business management.
Prime -2 to prime +1 is
proposed if small business is to be developed.
Government
The government may also give tax
breaks to those small businesses that pay their debts on time to encourage a culture
- f
loan repayment.
The government should investigate
how the National Credit Act affects loan disbursements to small businesses and keep improving its
- bjectives.
Collaboration between banks and
government in programmes like the Black Business Development Supplier Programme can be vehicles used to address the competitiveness of black businesses and the issue of collateral or guarantees in loan applications and advances.
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Clemence Hwarire
AFRICAN DEVELOPMENT FINANCE WORKSHOP 7-8 AUGUST 2012
+27 72-122-5976 clemhwarire@yahoo.com
By Clemence Hwarire